Special driving data recording method for new energy automobile and tire

Abstract

The invention discloses a driving data recording method special for a new energy automobile and a tire, and belongs to the technical field of new energy automobile tires. The method comprises the following steps: deploying a monitoring module to a tire of the new energy automobile to collect tire driving data of the tire; transmitting the tire driving data to a vehicle control unit and a preset cloud in real time based on a preset wireless transmission protocol; vehicle driving parameters, environment sensing data and historical maintenance records are obtained based on the new energy vehicle, and the tire driving data, the vehicle driving parameters, the environment sensing data and the historical maintenance records are processed based on the tire state analysis model to generate tire health degree and risk early warning; when the risk early warning exceeds a preset threshold value, the new energy vehicle is processed based on a preset vehicle control strategy; and estimating the remaining service life of the tire based on the tire health degree and a preset tire material degradation curve. According to the method, the tire driving data can be recorded, and the tire can be recommended according to the tire driving data.

Description

Technical Field

[0001] The present application relates to the technical field of smart tires, and in particular to a driving data recording method and tire specifically for new energy vehicles. Background Art

[0002] With the rapid development of new energy vehicles, tires, as key components that directly contact the vehicle with the road, have a direct impact on vehicle safety, energy efficiency and driving experience.

[0003] Traditional tire monitoring technology primarily relies on a single sensor (such as a tire pressure sensor) to acquire data and issue warnings based on simple threshold judgments. This results in limited data dimensions and insufficient analysis accuracy. For example, while existing tire pressure monitoring systems (TPMS) provide real-time feedback on tire pressure and temperature, they are unable to integrate vehicle dynamic parameters and environmental factors for a multi-dimensional health assessment.

[0004] Software-defined tire management systems (STPMS) technology is gaining popularity. It indirectly infers tire status by integrating vehicle dynamic data. However, this relies on the generalization capabilities of algorithmic models and is prone to misjudgment under complex operating conditions. For example, the wheel speed difference method struggles to accurately identify abnormal tire pressure when vehicle load fluctuates or the road surface is uneven. Furthermore, existing tire recommendations are often based on static parameter matching, lacking dynamic optimization for driving habits and environmental adaptability, resulting in insufficient adaptability.

[0005] Therefore, how to record tire driving data and tires with tire driving data becomes a technical problem that needs to be solved urgently. Summary of the Invention

[0006] The embodiments of the present application provide a driving data recording method and tire specifically for new energy vehicles, to solve the following technical problems: how to record tire driving data, and a tire having tire driving data.

[0007] In a first aspect, an embodiment of the present application provides a driving data recording method dedicated to new energy vehicles, which is applied to new energy vehicles, and the method includes: deploying a monitoring module to the tires of the new energy vehicle to collect tire driving data of the tires; wherein the monitoring module includes a temperature sensor, a pressure sensor, a three-axis acceleration sensor and a wireless communication unit, and the tire driving data includes temperature, pressure and three-axis acceleration; based on a preset wireless transmission protocol, the tire driving data is transmitted in real time to a vehicle control unit and a preset cloud; wherein the vehicle control unit includes a preset tire state analysis model; based on the new energy vehicle, vehicle driving parameters, environmental perception data and historical maintenance records are obtained, and the tire driving data, vehicle driving parameters, environmental perception data and historical maintenance records are processed based on the tire state analysis model to generate tire health and risk warnings; when the risk warning exceeds a preset threshold, the new energy vehicle is processed based on a preset vehicle control strategy; based on the tire health and a preset tire material degradation curve, the remaining service life of the tire is estimated.

[0008] In one implementation of the present application, a monitoring module is deployed to the tire of the new energy vehicle to collect tire driving data of the tire, specifically including: encapsulating the temperature sensor, pressure sensor, and three-axis acceleration sensor to generate a first monitoring module; connecting the first monitoring module to the wireless communication unit to generate the monitoring module; deploying the monitoring module to a preset position on the inner side of the crown of the tire to generate a monitoring belt that matches the curvature of the tire, and embedding it between the carcass structure layers during the tire forming stage; when the three-axis acceleration sensor detects that the acceleration exceeds a preset vehicle speed threshold, increasing the temperature sampling frequency of the temperature sensor, and drift compensating the pressure sensor based on the baseline pressure value of the tire in a stationary state; integrating acceleration, temperature, and pressure based on a time sequence to generate the tire driving data.

[0009] In one implementation of the present application, vehicle driving parameters, environmental perception data and historical maintenance records are obtained based on the new energy vehicle, and the tire driving data, vehicle driving parameters, environmental perception data and historical maintenance records are processed based on the tire state analysis model to generate tire health and risk warnings, specifically including: obtaining vehicle driving parameters from the CAN bus of the new energy vehicle; wherein the vehicle driving parameters include at least real-time vehicle speed, motor output torque and battery load status; obtaining environmental perception data through preset on-board environmental sensors; wherein the environmental perception data includes at least road humidity, outside temperature and air density; retrieving historical maintenance records through the cloud database; wherein the historical maintenance records include tire replacement cycle, tire pressure calibration record and wear repair log; preprocessing the tire driving data, vehicle driving parameters and environmental perception data to generate a standardized input vector; inputting the standardized input vector into the tire state analysis model to calculate the tire health score; and determining the risk warning based on the environmental perception data, vehicle driving parameters and tire health score.

[0010] In one implementation of the present application, the construction of the tire state analysis model specifically includes: constructing a hybrid model architecture including an LSTM network and a fully connected layer; wherein the LSTM network is used to process time series tire driving data, and the fully connected layer is used to fuse vehicle driving parameters and environmental perception data; determining training data; wherein the training data includes historical tire failure samples, normal wear samples and manually annotated health labels; training the hybrid model architecture through a preset supervised learning method and the training data; minimizing the mean square error loss between the predicted health and the actual health label based on a preset Adam optimizer; and outputting the tire state analysis model when the mean square error loss is less than a preset success threshold.

[0011] In one implementation of the present application, when the risk warning exceeds a preset threshold, the new energy vehicle is processed based on a preset vehicle control strategy, specifically including: calculating the suspension damping coefficient adjustment amount according to the pressure and temperature, and adjusting the tire support stiffness through the electronically controlled suspension system of the new energy vehicle; communicating with the motor control module of the new energy vehicle based on the risk warning, reducing the motor output power to a safety threshold range according to a preset ratio; generating a visual warning icon on the on-board interactive interface of the new energy vehicle, and broadcasting real-time risk warning information.

[0012] In one implementation of the present application, the remaining service life of the tire is calculated based on the tire health and a preset tire material degradation curve, specifically including: matching the material degradation curve corresponding to the rubber formula of the tire based on a preset material database; wherein the material degradation curve is used to describe the relationship between the change of elastic modulus with temperature and time; superimposing and analyzing the tire health and the material degradation curve to calculate the theoretical remaining wear thickness of the tire; and processing the remaining wear thickness based on the average speed and load data in the vehicle driving parameters to determine the remaining service life.

[0013] In one implementation of the present application, the method also includes: defining a multi-objective optimization function; wherein the objectives of the multi-objective optimization function include minimizing rolling resistance, maximizing grip on wet roads, and balancing wear distribution; setting constraints; wherein the constraints include tire size matching range, maximum load capacity, and speed level limit; obtaining the driving habits of the new energy vehicle; adjusting the weight coefficient of the multi-objective optimization function according to the driving habit data; searching the tire recommendation database for candidate tire models that meet the constraints through a preset genetic algorithm; processing each candidate tire model based on the multi-objective optimization function to calculate the fitness score corresponding to each candidate tire model.

[0014] In one implementation of the present application, after processing each candidate tire model based on the multi-objective optimization function to calculate the fitness score corresponding to each candidate tire model, the method also includes: setting a remaining service life threshold; when the remaining service life of the tire is lower than the remaining service life threshold, arranging the candidate tires in descending order according to the fitness score, and eliminating candidate tire models that do not meet the ambient temperature and / or road humidity requirements to generate a first alternative set; weighted matching the brand preference data in the user's historical replacement records with the first alternative set to generate a recommended tire model set including recommended models, performance comparison data and replacement urgency; and synchronizing the recommended tire model set to the on-board interactive interface of the new energy vehicle and the user account in the cloud.

[0015] In the second aspect, an embodiment of the present application also provides a driving data recording tire dedicated to new energy vehicles, which is applied to the above-mentioned driving data recording method dedicated to new energy vehicles. The tire includes: a flexible monitoring belt, embedded between the carcass structure layers of the tire; the flexible monitoring belt includes a flexible circuit board substrate, and a temperature sensor, a pressure sensor, a three-axis acceleration sensor and a wireless communication unit encapsulated on the flexible circuit board substrate; the temperature sensor is used to monitor the internal temperature of the tire, the pressure sensor is used to measure the air pressure inside the tire, and the three-axis acceleration sensor is used to detect the longitudinal, lateral and vertical acceleration of the tire; the wireless communication unit includes a dual-mode transmission module, which supports the main channel of the short-range wireless protocol and the backup channel of the long-range wireless protocol, and is used to transmit data from the temperature sensor, pressure sensor and three-axis acceleration sensor to the vehicle control unit and the cloud.

[0016] In one implementation of the present application, the three-axis acceleration sensor is connected to a temperature sensor, and when it is detected that the longitudinal acceleration exceeds a preset vehicle speed threshold, the sampling frequency of the temperature sensor is increased.

[0017] The embodiments of the present application provide a method and tire for recording driving data specifically for new energy vehicles, which have at least the following technical effects:

[0018] Data Collection and Transmission: Flexible circuit board packaging technology integrates sensors into the tire carcass, eliminating signal distortion associated with traditional external sensors due to tire deformation. Dynamic sampling frequency adjustment and pressure drift compensation significantly improve the accuracy of temperature, pressure, and acceleration data. Redundant transmission channels and a block-by-block verification mechanism effectively ensure the integrity and stability of data transmission, addressing communication reliability issues under complex operating conditions.

[0019] Risk assessment and control: Multi-source data fusion analysis based on a hybrid model architecture (LSTM network and fully connected layer) can accurately identify risks such as tire wear and temperature anomalies to a certain extent. It also adjusts the suspension stiffness and motor output power in real time through linkage with the vehicle control system, improving the vehicle's handling stability and safety under abnormal conditions.

[0020] Life prediction and recommendation: Combines tire material degradation characteristics with real-time driving data for detailed prediction of remaining service life. A multi-objective optimization algorithm dynamically balances rolling resistance, grip, and wear balance indicators, generating personalized recommendations based on user driving habits and environmental adaptability constraints, improving the scientific nature of tire selection and user adaptability.

[0021] In summary, this comprehensive technology system, encompassing multi-dimensional data collection, health status assessment, and intelligent decision-making, has enabled the evolution of tire management from passive response to proactive prevention. Through collaboration between the cloud and onboard terminals, risk warnings, control strategies, and replacement recommendations are synchronized in real time, effectively mitigating safety risks associated with tire failures while optimizing vehicle energy efficiency and range, driving advancements in intelligent operation and maintenance for new energy vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0023] Figure 1 A driving data recording method and tire flow chart specifically for new energy vehicles are provided in an embodiment of the present application. DETAILED DESCRIPTION

[0024] To make the purpose, technical solutions, and advantages of this application more clear, the technical solutions of this application will be clearly and completely described below in conjunction with the specific embodiments of this application and the corresponding drawings. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0025] The embodiments of the present application provide a driving data recording method and tire specifically for new energy vehicles, to solve the following technical problems: how to record tire driving data, and a tire having tire driving data.

[0026] The technical solutions proposed in the embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0027] Figure 1 This is a flow chart of recording tires for recording driving data for new energy vehicles provided in an embodiment of the present application. Figure 1 As shown, the embodiment of the present application provides a method for recording driving data for new energy vehicles, which specifically includes the following steps:

[0028] Step 1: Deploy a monitoring module to the tire of the new energy vehicle to collect tire driving data of the tire; wherein the monitoring module includes a temperature sensor, a pressure sensor, a three-axis acceleration sensor and a wireless communication unit, and the tire driving data includes temperature, pressure and three-axis acceleration.

[0029] Step 1.1: Encapsulate the temperature sensor, pressure sensor, and triaxial acceleration sensor using flexible circuit board packaging technology to generate a first monitoring module.

[0030] Flexible circuit board packaging technology uses a flexible, deformable circuit substrate (such as polyimide) to integrate sensors onto a flexible substrate, allowing it to conform to the tire's curved surface. A temperature sensor monitors internal tire temperature in real time, a pressure sensor measures tire pressure, and a triaxial accelerometer detects lateral, longitudinal, and vertical acceleration.

[0031] In a specific example, the pins of a temperature sensor (such as an NTC thermistor), a pressure sensor (such as a MEMS piezoresistive sensor), and a three-axis acceleration sensor (such as an ADXL345 chip) are soldered to corresponding interfaces of the flexible circuit board.

[0032] The three sensors are packaged into one with the flexible substrate through a vacuum lamination process to form a thin sheet structure (ie, the first monitoring module).

[0033] Step 1.2: Connect the first monitoring module to the wireless communication unit to generate the monitoring module.

[0034] The wireless communication unit refers to a communication module that supports Bluetooth Low Energy (BLE) or LoRa protocol, which is used to transmit sensor data to the vehicle terminal or the cloud in real time.

[0035] In a specific example, a communication interface is reserved on the flexible circuit board of the first monitoring module, the pins of the wireless communication unit (such as the ESP32 chip) are connected to the interface through conductive silver paste, and the connection part is fixed with epoxy resin glue to prevent falling off due to vibration.

[0036] It is understandable that after the connection is completed, the signal stability test of the communication link can be performed. When the data transmission bit error rate is lower than a certain threshold (usually set to 0.1%) when the tire rotates at high speed, it indicates that the communication module is functioning normally.

[0037] Step 1.3: Deploy the monitoring module to a preset position inside the crown of the tire to generate a monitoring strip that matches the curvature of the tire and embed it between the carcass structure layers during the tire forming stage.

[0038] The inner side of the crown refers to the inner surface of the area where the tire contacts the ground; the monitoring strip is a strip-shaped monitoring area arranged along the circumference of the tire, and its length is slightly shorter than the outer circumference of the tire.

[0039] In one specific example, during the tire building process, the monitoring modules are circumferentially attached to the tire crown between the carcass ply and the belt ply. Vulcanization bonds the monitoring modules to the rubber material, resulting in a complete rubber coating.

[0040] Step 1.4: When the three-axis acceleration sensor detects that the acceleration exceeds a preset vehicle speed threshold, the temperature sampling frequency of the temperature sensor is increased, and drift compensation is performed on the pressure sensor based on a reference pressure value of the tire in a stationary state.

[0041] The preset vehicle speed threshold is set according to the tire specifications (for example, the acceleration value corresponding to 80 km / h); the reference pressure value is the standard tire pressure value when the vehicle is stationary and the tires are cooled.

[0042] In a specific example, the longitudinal acceleration threshold of the triaxial acceleration sensor is set to 3m / s 2 (Corresponding to a vehicle speed of 80 km / h). When the acceleration exceeds the threshold, the temperature sensor's sampling frequency increases from 1 Hz to 10 Hz to capture the temperature rise caused by high-speed driving. Simultaneously, the pressure sensor reads a baseline pressure value (e.g., 2.5 bar) at rest every five minutes. Using this value as a reference, a Kalman filter algorithm is used to eliminate zero-point drift errors caused by long-term sensor operation. This Kalman filter algorithm is currently available and will not be described in detail here.

[0043] Step 1.5: Integrate acceleration, temperature, and pressure based on a time sequence to generate the tire driving data.

[0044] Time sequence integration means adding a timestamp to the data of each sensor and packaging it into structured data after aligning it by time axis.

[0045] In a specific example, a real-time clock (RTC) module is embedded in the wireless communication unit to annotate the acquisition time for temperature, pressure, and acceleration data. A data fusion algorithm is used to combine multi-dimensional data within the same time window into a single record. For example, the temperature (70°C), pressure (2.8 bar), and longitudinal acceleration (2.5 m / s²) at t = 10:00:00 are linked to form a single tire driving data record.

[0046] Step 2: Transmitting the tire driving data in real time to a vehicle control unit and a preset cloud based on a preset wireless transmission protocol; wherein the vehicle control unit includes a preset tire state analysis model.

[0047] Step 2.1, constructing a redundant transmission channel; wherein the redundant transmission channel includes a main channel and a backup channel, the main channel is a short-distance wireless protocol channel, and the backup channel is a long-distance wireless protocol transmission channel.

[0048] The primary channel is paired with the Bluetooth receiver of the vehicle control unit, and the backup channel is connected to the cloud server via the SIM card. The primary and backup channels work in parallel, with the primary channel being used for data transmission by default.

[0049] A dual-mode communication module is integrated into the vehicle control unit (such as an onboard ECU). The primary channel is configured for the BLE protocol, and the backup channel is configured for the LoRa protocol. The primary channel is bound to the vehicle terminal, while the backup channel is directly connected to the pre-set cloud. The primary and backup channels share the same data source (monitoring module), but transmit via independent antennas and frequency bands.

[0050] In one specific example, when designing redundant transmission channels, a new energy vehicle company, A, set the transmit power of the BLE primary channel to 4dBm (covering a 30-meter radius) and the transmit power of the LoRa backup channel to 20dBm (covering a 1.2-kilometer radius). When the vehicle is driving in an urban area, communication with the onboard terminal is prioritized over the BLE channel. When the vehicle enters a signal-blocked area (such as an underground garage), the LoRa channel is automatically activated to upload data to the cloud.

[0051] Step 2.2: Monitor the transmission stability of the primary channel based on a preset signal strength threshold, and start the backup channel when the signal strength of the primary channel is lower than the signal strength threshold.

[0052] The signal strength threshold is the critical signal strength value that triggers channel switching. It is usually set based on the minimum stable transmission requirements of the communication protocol. For example, the signal strength threshold of the BLE protocol can be set to -80dBm.

[0053] Transmission stability monitoring determines whether the main channel meets data transmission requirements by calculating the received signal strength indicator (RSSI) and bit error rate (BER) in real time.

[0054] When the RSSI is detected to be lower than the threshold for three consecutive times, the channel start instruction is triggered.

[0055] In a specific example, the control unit of a vehicle model B sets the BLE signal strength threshold to -80dBm. When the vehicle enters a tunnel and the BLE signal strength drops to -85dBm, the LoRa backup channel is activated.

[0056] Step 2.3: Divide the tire driving data into blocks and encapsulate them into multiple data packets, and add a check code to each data packet.

[0057] Block encapsulation refers to dividing the complete tire driving data into multiple data packets according to a preset size, for example, each data packet contains a 256-byte payload.

[0058] Checksum is a code used to verify data integrity.

[0059] A single tire driving data (such as timestamp + temperature + pressure + acceleration) is split into three data packets in sequence: the header (timestamp), the middle (sensor value), and the tail (check code).

[0060] Add checksum: Add a checksum to each data packet and append it to the end of the data packet.

[0061] In a specific example, a single tire driving data generated by a certain test module is "2023-10-01 10:00:00, 70°C, 2.8bar, 2.5m / s2", with a total length of 64 bytes. It can be broken down into:

[0062] Header data packet (20 bytes): contains timestamp and checksum;

[0063] Middle data packet (40 bytes): contains temperature, pressure, acceleration data and check code;

[0064] Tail data packet (4 bytes): reserved for redundancy check bits.

[0065] Step 2.4: Synchronously transmit different data packets through the primary channel and the backup channel, and perform data integrity verification using a preset checksum at the receiving end of the vehicle control unit and the cloud.

[0066] When the backup channel is enabled, synchronous transmission is started. Synchronous transmission means that the main channel and the backup channel transmit different data packets at the same time. For example, the main channel transmits the head and middle data packets, and the backup channel transmits the tail and redundant backup packets.

[0067] Data integrity check is for the receiving end to verify whether the data packet is complete through the check code. If the check fails, it requests retransmission.

[0068] The transmission strategy is that the main channel prioritizes the transmission of key data (such as the head and middle), and the backup channel transmits auxiliary data (such as the tail and redundant packets).

[0069] The receiving end processes the data packets received by the vehicle terminal and the cloud server respectively, and verifies the integrity of the data packet through the checksum. If a data packet fails the checksum, a retransmission request is sent to the sending end.

[0070] In a specific example, a cloud server C receives a header packet from the primary channel (CRC check passed) and a tail packet from the backup channel (CRC check failed). It extracts the tail packet from the redundant backup of the backup channel, rechecks it, and completes data splicing to generate a complete tire driving data record.

[0071] Step 2: Obtain vehicle driving parameters, environmental perception data, and historical maintenance records based on the new energy vehicle, and process the tire driving data, vehicle driving parameters, environmental perception data, and historical maintenance records based on the tire condition analysis model to generate tire health and risk warnings.

[0072] Step 2.1: Acquire vehicle driving parameters from the CAN bus of the new energy vehicle; wherein the vehicle driving parameters include at least real-time vehicle speed, motor output torque, and battery load status.

[0073] The CAN bus is a communication network between electronic control units (ECUs) within a vehicle, used to transmit real-time vehicle status information.

[0074] In a specific example:

[0075] Data acquisition: Access the CAN bus via the OBD-II interface and read the following parameters in real time:

[0076] Real-time vehicle speed: parses CAN message ID 0x0CFF0000 and converts it into a physical quantity (km / h). The sampling frequency is 10Hz.

[0077] Motor output torque: Analyze ID 0x0CF00400 and convert it to torque value (Nm) with an accuracy of ±2%.

[0078] Battery load status: Parse ID 0x0CF00300 to obtain the current battery load percentage.

[0079] Abnormal detection: If the vehicle speed exceeds the vehicle design limit (such as 300km / h) for five consecutive times, it is determined to be a signal abnormality and cache data replacement is enabled.

[0080] Step 2.2: Acquire environmental perception data through a preset vehicle-mounted environmental sensor; wherein the environmental perception data at least includes road humidity, external temperature, and air density.

[0081] On-board environmental sensors are used to monitor external environmental parameters and evaluate their real-time impact on tire performance.

[0082] It can be understood that the environmental data and the vehicle driving parameters are aligned through a unified timestamp.

[0083] Step 2.3: Retrieve historical maintenance records from the cloud database; wherein the historical maintenance records include tire replacement cycles, tire pressure calibration records, and wear repair logs.

[0084] Historical maintenance records are used to correct the timeliness of health assessments and reflect the actual usage history of the tire.

[0085] In a specific example:

[0086] Data query and matching:

[0087] Initiate a request to the cloud using the vehicle VIN code to obtain the following data:

[0088] Tire replacement cycle: record tire brand, installation date, cumulative mileage and replacement reason (such as wear and tear, bulge).

[0089] Tire pressure calibration records: time, calibration value (bar) and operating equipment model of the last five calibrations.

[0090] Wear repair log: tire repair location (such as the left area of the tire crown), repair process (patch / mushroom nail) and post-repair inspection results.

[0091] Weight allocation: Assign weight coefficients based on the time of maintenance records (for example, the weight of records within 1 year is 1.0, and it decays by 0.15 every additional year).

[0092] Step 2.4: Preprocess the tire driving data, vehicle driving parameters, and environment perception data to generate a standardized input vector.

[0093] Normalizing input vectors converts multi-source heterogeneous data into a unified format that the model can process.

[0094] In a specific example:

[0095] Data cleaning and completion:

[0096] Median filtering (window size 3 seconds) is used to eliminate impulse noise on tire temperature data.

[0097] If the road surface moisture data is missing, it is filled in by interpolation using the adjacent vehicle data from the same time period.

[0098] The vehicle speed is normalized to 0-1 (based on the vehicle's maximum design speed, such as 200km / h).

[0099] The road surface moisture is discretely coded as 0 (dry, 0-1 mm), 1 (wet, 1-3 mm), and 2 (water accumulation, >3 mm).

[0100] The historical maintenance weight is directly used as an input feature according to the time decay coefficient (for example, the weight of records 2 years ago is 0.7).

[0101] Time series alignment: Accurately align the 60 time steps of tire data (1 point per minute) with vehicle parameters and environmental data along the time axis.

[0102] Step 2.5: Input the normalized input vector into the tire condition analysis model to calculate a tire health score.

[0103] Model processing flow:

[0104] LSTM layer input: 60 time steps × 5 features (temperature, pressure, and triaxial acceleration).

[0105] Fully connected layer input: LSTM output features (128 dimensions) + vehicle parameters (3 dimensions) + environmental data (3 dimensions) + maintenance weights (1 dimension), totaling 135 dimensions.

[0106] In a specific case, it also includes dynamic modification rules:

[0107] If historical maintenance records show that the tire has been used for more than the manufacturer's recommended life (such as 5 years), the health score is multiplied by a time decay coefficient of 0.8.

[0108] If there is a tire pressure calibration record within the last three months, an additional 5 points will be added to the score.

[0109] Step 3.6: Determine risk warning based on environmental perception data, vehicle driving parameters, and tire health score.

[0110] Risk warning triggers graded alerts based on a combination of health scores and real-time environment / vehicle status.

[0111] In a specific example:

[0112] Level 1 warning (emergency): Health score ≤ 40 points and (vehicle speed ≥ 100 km / h or road humidity = Level 2).

[0113] Level 2 warning (high risk): Health score ≤ 60 points and (battery load ≥ 90% or external temperature ≥ 45°C).

[0114] Level 3 warning (prompt): Health level ≤ 80 points and accumulated mileage exceeds 80% of the recommended replacement mileage.

[0115] The construction process of the tire condition analysis model is as follows:

[0116] A1. Construct a hybrid model architecture including an LSTM network and a fully connected layer; wherein the LSTM network is used to process time series tire driving data, and the fully connected layer is used to integrate vehicle driving parameters and environmental perception data.

[0117] The LSTM network (Long Short-Term Memory Network) is a variant of the recurrent neural network. It controls information flow through forget gates, input gates, and output gates, and is suitable for processing the dynamic characteristics of time series data such as tire temperature and pressure.

[0118] The input dimensions are set as the time step (such as sensor data within 60 seconds) and the number of features (temperature, pressure, and three-axis acceleration, a total of 5 features).

[0119] Build a 3-layer LSTM network with 128 neurons in each layer. Set the initial value of the forget gate bias to 1.0 to prevent gradient vanishing, and fix the time step to 60 (corresponding to data sampled once per minute).

[0120] The LSTM layer outputs the hidden state of each time step, which is compressed into a single feature vector (dimension 128) through the average pooling layer.

[0121] The fully connected layer is used to fuse vehicle driving parameters (such as speed and torque) and environmental perception data (such as road humidity) and combine them with the time series features extracted by LSTM.

[0122] Data alignment: The feature vector (dimension 128) output by LSTM is concatenated with vehicle driving parameters (3 features), environmental data (3 features), and maintenance weights (1 dimension) into a unified input vector (dimension 135).

[0123] The first layer has 64 neurons and uses ReLU (rectified linear unit) as the activation function to extract nonlinear features.

[0124] The second layer has 32 neurons and uses ReLU as the activation function to further reduce the dimensionality.

[0125] Output layer: 1 neuron, using Sigmoid as the activation function to scale the output value to a health score of 0 to 100.

[0126] A2. Determine training data; wherein the training data includes historical tire failure samples, normal wear samples, and manually annotated health labels.

[0127] The training data must cover the entire life cycle of the tire, including extreme failure and normal wear scenarios, and the health label quantifies the remaining life of the tire based on expert experience.

[0128] Extract the time series data of tire temperature, pressure, and acceleration from the vehicle terminal, and synchronously record the vehicle speed, torque and road humidity collected by the environmental sensor of the vehicle OBD interface.

[0129] A3. Train the hybrid model architecture using a preset supervised learning method and the training data.

[0130] In a specific example, 10,000 groups of samples were divided into training set, validation set, and test set according to the ratio of 7:2:1, and the mean square error (MSE) was used to calculate the difference between the predicted health and the actual label.

[0131] The training strategy can be set as:

[0132] Batch size: 32 samples / batch.

[0133] Learning rate: Initial value is 0.001, and decays by 50% every 10 epochs.

[0134] Early stopping mechanism: If the validation set loss does not decrease for 5 consecutive epochs, the training is terminated.

[0135] A4. Minimize the mean square error loss between the predicted health and the actual health label based on the preset Adam optimizer.

[0136] The Adam optimizer combines momentum method and adaptive learning rate, which is suitable for optimizing non-stationary objective functions.

[0137] In a specific example:

[0138] Parameter settings: first-order moment estimation decay rate β1 = 0.9, second-order moment estimation decay rate β2 = 0.999, epsilon = 1e-7.

[0139] Gradient clipping: Set the gradient norm threshold to 5.0 to prevent gradient explosion.

[0140] Weight regularization: L2 regularization (coefficient 0.01) is applied to the weights of the fully connected layer to suppress overfitting.

[0141] A5. When the mean square error loss is less than a preset success threshold, output the tire condition analysis model.

[0142] The preset success threshold is that the mean square error of the validation set is less than 1.0 points (that is, the average deviation between the predicted health and the actual label is less than 1 point).

[0143] For example, if the validation set loss fluctuation range is less than 0.1 for three consecutive epochs, the model is considered converged. The model parameters with the smallest validation set loss are selected as the tire condition analysis model.

[0144] Step 3: When the risk warning exceeds a preset threshold, the new energy vehicle is processed based on a preset vehicle control strategy.

[0145] Step 3.1: Calculate the suspension damping coefficient adjustment amount according to the pressure and temperature, and adjust the tire support stiffness through the electronically controlled suspension system of the new energy vehicle.

[0146] The suspension damping coefficient adjustment refers to the suspension system stiffness adjustment parameter dynamically calculated based on the real-time tire pressure and temperature, and is used to optimize the contact performance between the tire and the road.

[0147] In a specific example:

[0148] Based on the tire pressure (unit: bar) and temperature (unit: ° C), a basic damping coefficient is determined by a preset mapping table (for example, a pressure of 2.5 bar and a temperature of 70° C. correspond to a damping coefficient C0).

[0149] The compensation coefficient α is calculated based on the pressure change rate (ΔP / Δt) and the temperature gradient (ΔT / Δt), and the adjustment amount is C=C0×(1+α).

[0150] The adjustment instructions are sent to the electronically controlled suspension system via the CAN bus, and the target damping coefficient is written into the suspension control module (such as the CDC continuous damping control system).

[0151] Step 3.2: Based on the risk warning, the motor control module of the new energy vehicle is communicated to reduce the motor output power to a safety threshold range according to a preset ratio.

[0152] The safety threshold range refers to the maximum allowable output power dynamically calculated based on the current state of the vehicle (such as vehicle speed and battery load).

[0153] In a specific example:

[0154] High risk warning: Immediately reduce the motor output power to 50% of the current value.

[0155] Medium-risk warning: Reduce power in stages, by 10% every 5 seconds, until a safety threshold is reached (e.g., 70% of maximum power).

[0156] Step 3.3: Generate a visual warning icon on the onboard interactive interface of the new energy vehicle and broadcast real-time risk warning information.

[0157] The visual warning icon dynamically reflects the risk level through color (red / yellow / green) and graphics, and the voice prompt information contains the specific risk type and response suggestions.

[0158] In a specific example:

[0159] High-risk warning: The central control screen displays a flashing red tire icon with the text "Stop and check immediately" superimposed.

[0160] Medium-risk warning: A yellow exclamation mark icon is displayed with the text "Recommend slowing down and contacting service."

[0161] Synthesized voice messages are played through the vehicle's audio system, containing information about risky locations (e.g., "Abnormal left front tire pressure") and recommended actions (e.g., "Please reduce the vehicle speed to 60 km / h").

[0162] Step 4: Calculate the remaining service life of the tire based on the tire health and a preset tire material degradation curve.

[0163] Step 4.1: Matching a material degradation curve corresponding to the rubber formula of the tire based on a preset material database; wherein the material degradation curve is used to describe the relationship between the elastic modulus and the temperature and time.

[0164] The material database stores experimental data curves showing how the elastic modulus of different rubber formulations changes with temperature and time. Each curve corresponds to the aging law of tire materials of a specific formulation.

[0165] In a specific example:

[0166] The tire model and rubber compound code (such as "NR-SBR-70A") are obtained through the RFID tag or QR code on the side of the tire, and the corresponding degradation curve is retrieved from the material database.

[0167] The degradation curve is stored in the form of a two-dimensional graph, with the horizontal axis representing the cumulative usage time (hours) and the vertical axis representing the elastic modulus attenuation rate (%), and is accompanied by a temperature correction factor table (for example, the aging rate at 70°C is 3.2 times that of 25°C).

[0168] The degradation curve is weighted according to the tire's historical temperature data (collected in step 3). For example, if a tire operates at 50°C for 70% of the time and at 30°C for 30% of the time, the equivalent aging time is calculated.

[0169] Step 4.2: Superimpose the tire health and material degradation curves to calculate the theoretical remaining wear thickness of the tire.

[0170] Overlay analysis combines the health score (reflecting the current wear status) with the material degradation curve (predicting the future aging rate) to estimate the remaining usable thickness of the tire tread.

[0171] In a specific example:

[0172] The health score (0 to 100 points) is linearly mapped to the current tread remaining thickness percentage (for example, a health score of 80 points corresponds to a remaining thickness of 80% of the initial value).

[0173] According to the material degradation curve, the elastic modulus attenuation rate is integrated over time with the current time as the starting point to predict the thickness reduction corresponding to every 1,000 kilometers of driving in the future.

[0174] Assuming the initial tire thickness is 8mm, the current health score of 60 points corresponds to a remaining thickness of 4.8mm. Combined with the prediction that the thickness will decrease by 1.2mm every 5,000 kilometers of driving in the future, the theoretical remaining wear thickness is 3.6mm.

[0175] Step 4.3: Process the remaining wear thickness based on the average vehicle speed and load data in the vehicle driving parameters to determine the remaining service life.

[0176] The remaining service life needs to be corrected based on the actual use conditions (such as accelerated wear due to high-speed driving and increased deformation due to heavy loads).

[0177] In a specific example:

[0178] Vehicle speed correction coefficient: Preset vehicle speed wear coefficient table, for example, when the average vehicle speed is 80km / h, the wear rate is 1.8 times that of 40km / h.

[0179] The wear rate is linearly corrected based on the ratio of vehicle load to tire rated load (e.g., a 20% overload increases the wear rate by 30%).

[0180] Divide the theoretical remaining thickness of 3.6mm by the corrected wear per kilometer (for example, 0.00024mm / km) to obtain a remaining mileage of 15,000 kilometers, which is converted into a remaining service life (assuming an average daily mileage of 200 kilometers, the remaining service life is 75 days).

[0181] In this step, the tire's QR code is scanned to obtain the formula code "NR-EPDM-65B," and the corresponding degradation curve is retrieved from the database. This formula exhibits a 12% elastic modulus decay every 1000 hours at 60°C. Based on historical data, this tire operates 70% of the time at -18°C (cold chain environment) and 30% at 25°C (loading and unloading). The calculated equivalent aging rate is 0.35 times the standard curve.

[0182] The current health score is 72 points, and the mapped remaining thickness is 5.76 mm (initial 8 mm × 72%).

[0183] According to the equivalent degradation curve, it is predicted that the thickness decreases by 0.48 mm for every 1,000 kilometers of driving.

[0184] The average vehicle speed is 65 km / h (correction factor 1.5), the load is 110% of the rated value (correction factor 1.2), and the comprehensive wear rate is corrected to 0.48 mm / 1000 km×1.5×1.2=0.864 mm / 1000 km.

[0185] Remaining mileage = 5.76mm ÷ 0.864mm / 1000km ≈ 6,667 kilometers. Based on an average daily mileage of 300 kilometers, the remaining service life is approximately 22 days.

[0186] The vehicle system prompts that "the remaining life of the left front tire is 22 days" and recommends making an appointment for replacement within 15 days.

[0187] After determining that the tire needs to be replaced, the application also includes the following method for recommending suitable tires based on the customer's driving habits and driving environment.

[0188] Step B: processing the tire driving data, vehicle driving parameters, and environmental perception data based on a preset multi-objective optimization algorithm and a tire recommendation database to generate a fitness score.

[0189] Step B1, defining a multi-objective optimization function; wherein the objectives of the multi-objective optimization function include minimizing rolling resistance, maximizing grip on wet roads, and balancing wear distribution.

[0190] The multi-objective optimization function is a mathematical model that optimizes multiple performance indicators simultaneously, and is used to quantify the comprehensive performance of tires in rolling resistance, grip and wear balance.

[0191] In a specific example:

[0192] Minimize rolling resistance: The energy consumption coefficient is calculated based on the tire tread material and pattern design parameters. The lower the target value, the greater the potential for improving battery life.

[0193] Maximizing grip on wet roads: Calculates the friction coefficient based on parameters such as groove depth and rubber hardness. A higher target value indicates better braking performance.

[0194] Balanced wear distribution: Evaluated by the tread temperature distribution variance and pressure distribution uniformity indicators. The smaller the variance, the more uniform the wear.

[0195] Function construction: Normalize the three objectives to a score between 0 and 1, and perform weighted summation to generate a comprehensive optimization target value. The initial weights are set to 40% for rolling resistance, 40% for grip, and 20% for wear leveling.

[0196] Step B2: setting constraints; wherein the constraints include tire size matching range, maximum load capacity and speed grade limit.

[0197] Constraints are hard parameter limits that the tire must meet to ensure that the recommended model is compatible with the vehicle.

[0198] Size matching range: Hub diameter deviation is allowed to be ±0.5 inches, and section width deviation is ±10%.

[0199] Maximum load capacity: The rated load of the candidate tire must be ≥ 110% of the vehicle's maximum gross mass (including load).

[0200] Speed rating limit: The speed rating of the candidate tire (e.g. V-rated 240km / h) must be ≥ 120% of the vehicle's maximum design speed.

[0201] Step B3: Acquire the driving habits of the new energy vehicle.

[0202] Driving habits include characteristics such as acceleration frequency, average speed, and braking intensity, which reflect the actual usage pattern of the vehicle.

[0203] Data collection: Extract driving data from the vehicle terminal for the past 90 days, including:

[0204] The percentage of rapid acceleration (acceleration ≥ 2m / s2).

[0205] Average daily high-speed driving (≥80km / h) time.

[0206] Average braking deceleration and frequency.

[0207] Driving habits are classified into "aggressive", "smooth" and "conservative". For example, if the proportion of sudden acceleration is greater than 15%, it is aggressive.

[0208] Step B4: adjusting the weight coefficients of the multi-objective optimization function according to the driving habit data.

[0209] The weight coefficient adjustment is used to prioritize the core needs of different driving habits.

[0210] Aggressive driving: Increases grip weighting to 50% and reduces rolling resistance weighting to 30%.

[0211] Smooth driving: Maintain the initial weights (rolling resistance 40%, grip 40%, wear leveling 20%).

[0212] Conservative driving: Increase the weight of wear leveling to 30% and reduce the weight of grip to 30%.

[0213] Step B5: searching the tire recommendation database for candidate tire models that meet the constraint conditions using a preset genetic algorithm.

[0214] Genetic algorithms simulate the biological evolution process to screen out candidate tires that meet the constraints and have high optimization target values.

[0215] Randomly select any models from the tire database as the initial population.

[0216] Calculate the constraint compliance (such as size matching, load capacity) of each model and eliminate non-compliant items.

[0217] The parameters of the remaining models (such as pattern depth and rubber formula) are simulated by genetic crossover and random mutation to generate a new generation of population.

[0218] Repeat fitness evaluation and population update until the change rate of the optimal solution for a certain number of consecutive iterations is less than the preset threshold.

[0219] Step B6: Process each candidate tire model based on the multi-objective optimization function to calculate a fitness score corresponding to each candidate tire model.

[0220] Extract the rolling resistance coefficient (such as EU label grade C), wet grip index (such as 1.2G), and wear test data (such as 1.5mm wear after 50,000 kilometers) of the candidate tires.

[0221] Substituting the three indicators into the optimization function, for example, a tire's rolling resistance scores 0.8 points, grip scores 0.9 points, and wear balance scores 0.7 points, the comprehensive score is 0.8×0.4+0.9×0.4+0.7×0.2=0.82 points.

[0222] Arrange the candidate tires in descending order of fit score and mark the key performance parameters.

[0223] In a specific example, a certain car platform B recommends suitable tires for the vehicle:

[0224] Analysis shows that the vehicle's rapid acceleration accounts for 18% and the average daily high-speed driving time is 2 hours, which is judged as "aggressive driving".

[0225] The grip weighting is increased to 50%, the rolling resistance weighting is reduced to 30%, and the wear leveling is maintained at 20%.

[0226] 42 candidate models meeting the requirements of 215 / 55R17 size, load ≥750kg, and speed grade ≥V were selected from 1,000 tire models.

[0227] Model X scored 0.75 points (C grade) for rolling resistance, 0.95 points (AA grade) for grip, and 0.65 points for wear leveling. The overall score was 0.75×0.3+0.95×0.5+0.65×0.2=0.815 points, ranking first.

[0228] The platform gives priority to purchasing model X.

[0229] Step C: determining a recommended tire model set based on the ranking results of the fitness scores and the remaining service life.

[0230] Step C1: Set a remaining service life threshold.

[0231] The remaining service life threshold is a tire replacement trigger value set according to safety standards or user-defined requirements, usually based on the remaining mileage or days.

[0232] Industry standard setting: Refer to the replacement standards recommended by tire manufacturers (such as remaining tread thickness ≤ 1.6mm), and set the remaining service life threshold to 15 days (calculated based on an average daily mileage of 200 kilometers).

[0233] The threshold is dynamically modified based on the frequency of vehicle use. For example, if the average daily driving distance in the last 30 days is 300 kilometers, the threshold is shortened to 10 days.

[0234] Step C2: When the remaining service life of the tire is lower than the remaining service life threshold, the candidate tires are sorted in descending order according to the fitness score, and candidate tire models that do not meet the ambient temperature and / or road humidity requirements are eliminated to generate a first candidate set.

[0235] The first candidate set is a list of candidate tires that meet environmental adaptability requirements (such as temperature and humidity) and are arranged in descending order of fitness scores.

[0236] In a specific case,

[0237] Models whose applicable temperature range does not include the current region's annual average temperature ±10°C are eliminated (for example, a tire with an applicable temperature range of -20°C to 50°C and the current region's annual average temperature is 35°C is retained).

[0238] Eliminate models whose wetland grip level is lower than the corresponding requirement of the current area's annual rainfall (for example, areas with annual rainfall > 1000mm require a wetland grip level ≥ B).

[0239] Arrange in descending order of fit score. In case of the same score, give priority to the model with lower rolling resistance.

[0240] Step C3: Perform weighted matching on the brand preference data in the user's historical replacement records and the first candidate set to generate a recommended tire model set including recommended models, performance comparison data, and replacement urgency.

[0241] User brand preference data is extracted based on historical replacement records, and the ranking priority of preferred brands is improved through weighting.

[0242] Preference weight calculation: Count the proportion of each brand in the user's past five replacement records. For example, if brand X accounts for 80%, its weight coefficient is 0.8.

[0243] Multiply the candidate tire's fit score by (1 + brand weight). For example, if the original fit score of brand X tire is 0.85, the adjusted score is 0.85 × 1.8 = 1.53.

[0244] The urgency is determined by the difference between the remaining service life and the threshold:

[0245] Urgent (difference ≤ 3 days): Red mark, immediate replacement is recommended.

[0246] Recommendation (3 days < difference ≤ 7 days): Yellow mark, it is recommended to replace it this week.

[0247] Observation (difference > 7 days): Green mark, it is recommended to replace it during the next maintenance.

[0248] Step C4: Synchronize the recommended tire model set to the vehicle-mounted interactive interface of the new energy vehicle and the user account in the cloud.

[0249] Data synchronization ensures that users obtain consistent recommendation information on multiple terminals and supports the connection between online and offline replacement services.

[0250] In a specific case,

[0251] Displays the fit scores, price ranges, and urgency indicators of the top three recommended models. Click a model to view a performance comparison radar chart (rolling resistance, grip, and wear resistance).

[0252] The recommendation list is linked to the inventory information of nearby cooperative service outlets, displaying available models and reservation status in real time. The recommendation history is recorded in the user account for subsequent service tracking.

[0253] In a specific example, the remaining service life of user C's vehicle tire is 8 days (threshold 15 days):

[0254] From the top 50 candidate tires ranked by adaptability scores, three models that are not suitable for the high temperature and high humidity environment in the south (the upper limit of applicable temperature is 45°C < the local summer extreme temperature of 48°C) were eliminated.

[0255] The remaining 47 models are arranged in descending order of scores, with the top five scoring 0.92, 0.89, 0.87, 0.85, and 0.83 respectively.

[0256] In user C's historical replacement records, brand Y accounts for 60% and brand Z accounts for 40%.

[0257] Brand Y tires (original score 0.89) received an adjusted score of 0.89×1.6=1.424, rising to first place.

[0258] Brand Z tires (original score 0.87) received an adjusted score of 0.87×1.4=1.218, maintaining third place.

[0259] The urgency is marked as "recommendation" (difference 7 days), and the top three recommended models are brand YA model (1.424 points), brand XB model (0.92 points), and brand ZC model (1.218 points).

[0260] Performance comparison data marked brand YA type wet grip AA level, rolling resistance B level.

[0261] The car's central control screen displays a recommendation list, with brand YA at the top and marked "Frequently selected brand preferred."

[0262] The user’s mobile phone APP simultaneously pushes the message: “It is recommended to change to brand YA model, which is in stock at the nearby D service outlet.”

[0263] The present application also includes a tire for recording driving data for new energy vehicles, the tire comprising:

[0264] A flexible monitoring belt is embedded between the carcass structure layers of the tire.

[0265] The flexible monitoring belt includes a flexible circuit board substrate, a temperature sensor, a pressure sensor, a three-axis acceleration sensor and a wireless communication unit packaged on the flexible circuit board substrate.

[0266] The temperature sensor is used to monitor the internal temperature of the tire, the pressure sensor is used to measure the air pressure in the tire, and the three-axis acceleration sensor is used to detect the longitudinal, lateral and vertical accelerations of the tire.

[0267] The wireless communication unit includes a dual-mode transmission module that supports a main channel for a short-range wireless protocol and a backup channel for a long-range wireless protocol, and is used to transmit data from temperature sensors, pressure sensors, and three-axis acceleration sensors to the vehicle control unit and the cloud.

[0268] The three-axis acceleration sensor is connected to the temperature sensor, and when the longitudinal acceleration is detected to exceed a preset vehicle speed threshold, the sampling frequency of the temperature sensor is increased.

Claims

1. A driving data recording method for new energy vehicles, applied to new energy vehicles, characterized in that: The method comprises: Deploying a monitoring module to the tires of the new energy vehicle to collect tire driving data of the tires; wherein the monitoring module includes a temperature sensor, a pressure sensor, a three-axis acceleration sensor and a wireless communication unit, and the tire driving data includes temperature, pressure and three-axis acceleration; The tire driving data is transmitted in real time to a vehicle control unit and a preset cloud based on a preset wireless transmission protocol; wherein the vehicle control unit includes a preset tire state analysis model; Acquiring vehicle driving parameters, environmental perception data, and historical maintenance records based on the new energy vehicle, and processing the tire driving data, vehicle driving parameters, environmental perception data, and historical maintenance records based on the tire condition analysis model to generate tire health and risk warnings; When the risk warning exceeds a preset threshold, processing the new energy vehicle based on a preset vehicle control strategy; The remaining service life of the tire is estimated based on the tire health and a preset tire material degradation curve.

2. A method for recording driving data for new energy vehicles according to claim 1, characterized in that: Deploying a monitoring module to the tires of the new energy vehicle to collect tire driving data of the tires specifically includes: Encapsulating the temperature sensor, pressure sensor, and triaxial acceleration sensor to generate a first monitoring module; connecting the first monitoring module to the wireless communication unit to generate the monitoring module; Deploy the monitoring module to a preset position inside the crown of the tire to generate a monitoring strip that matches the curvature of the tire and is embedded between the carcass structure layers during the tire building stage; When the three-axis acceleration sensor detects that the acceleration exceeds a preset vehicle speed threshold, the temperature sampling frequency of the temperature sensor is increased, and drift compensation is performed on the pressure sensor based on a reference pressure value of the tire in a stationary state; Acceleration, temperature, and pressure are integrated based on a time sequence to generate the tire travel data.

3. The method for recording driving data for new energy vehicles according to claim 1, characterized in that: Acquiring vehicle driving parameters, environmental perception data, and historical maintenance records based on the new energy vehicle, and processing the tire driving data, vehicle driving parameters, environmental perception data, and historical maintenance records based on the tire condition analysis model to generate tire health and risk warnings, specifically including: Acquiring vehicle driving parameters from the CAN bus of the new energy vehicle; wherein the vehicle driving parameters include at least real-time vehicle speed, motor output torque and battery load status; Acquiring environmental perception data through a preset vehicle-mounted environmental sensor; wherein the environmental perception data includes at least road humidity, external temperature, and air density; Retrieving historical maintenance records from the cloud database; wherein the historical maintenance records include tire replacement cycles, tire pressure calibration records, and wear repair logs; Preprocessing the tire driving data, vehicle driving parameters and environment perception data to generate a standardized input vector; Inputting the normalized input vector into the tire condition analysis model to calculate a tire health score; Determine risk warnings based on environmental perception data, vehicle driving parameters and tire health scores.

4. A method for recording driving data for new energy vehicles according to claim 3, characterized in that: The construction of the tire state analysis model specifically includes: Constructing a hybrid model architecture including an LSTM network and a fully connected layer; wherein the LSTM network is used to process time series tire driving data, and the fully connected layer is used to integrate vehicle driving parameters and environmental perception data; Determine training data; wherein the training data includes historical tire failure samples, normal wear samples, and manually annotated health labels; Training the hybrid model architecture using a preset supervised learning method and the training data; Minimize the mean squared error loss between the predicted health and the actual health labels based on the preset Adam optimizer; When the mean square error loss is less than a preset success threshold, the tire condition analysis model is output.

5. The method for recording driving data for new energy vehicles according to claim 1, characterized in that: When the risk warning exceeds a preset threshold, the new energy vehicle is processed based on a preset vehicle control strategy, specifically including: Calculating a suspension damping coefficient adjustment amount based on the pressure and temperature, and adjusting tire support stiffness through the electronically controlled suspension system of the new energy vehicle; Based on the risk warning, the system communicates with the motor control module of the new energy vehicle and reduces the motor output power to a safe threshold range according to a preset ratio; A visual warning icon is generated on the on-board interactive interface of the new energy vehicle, and real-time risk warning information is broadcast.

6. The method for recording driving data for new energy vehicles according to claim 1, characterized in that: Calculating the remaining service life of the tire based on the tire health and a preset tire material degradation curve specifically includes: Matching a material degradation curve corresponding to the rubber formula of the tire based on a preset material database; wherein the material degradation curve is used to describe the relationship between the elastic modulus and temperature and time; Overlaying and analyzing the tire health and material degradation curves to calculate the theoretical remaining wear thickness of the tire; The remaining wear thickness is processed based on average vehicle speed and load data among vehicle driving parameters to determine the remaining service life.

7. The method for recording driving data for new energy vehicles according to claim 1, characterized in that: The method further comprises: Defining a multi-objective optimization function; wherein the objectives of the multi-objective optimization function include minimizing rolling resistance, maximizing grip on wet roads, and balancing wear distribution; Setting constraints; wherein the constraints include tire size matching range, maximum load capacity and speed rating limit; Obtaining the driving habits of the new energy vehicle; adjusting the weight coefficients of the multi-objective optimization function according to the driving habit data; Searching the tire recommendation database for candidate tire models that meet the constraint conditions using a preset genetic algorithm; Each candidate tire model is processed based on the multi-objective optimization function to calculate a fitness score corresponding to each candidate tire model.

8. The method for recording driving data for new energy vehicles according to claim 7, characterized in that: After processing each candidate tire model based on the multi-objective optimization function to calculate a fitness score corresponding to each candidate tire model, the method further includes: Setting a remaining useful life threshold; When the remaining service life of the tire is lower than the remaining service life threshold, the candidate tires are arranged in descending order according to the fitness scores, and candidate tire models that do not meet the ambient temperature and / or road humidity requirements are eliminated to generate a first candidate set; Performing weighted matching on the brand preference data in the user's historical replacement records with the first candidate set to generate a recommended tire model set including recommended models, performance comparison data, and replacement urgency; The recommended tire model set is synchronized to the vehicle interactive interface of the new energy vehicle and the user account in the cloud.

9. A tire for recording driving data specifically for new energy vehicles, applied to a method for recording driving data specifically for new energy vehicles as claimed in claims 1 to 8, characterized in that: The tire comprises: a flexible monitoring belt embedded between the carcass structure layers of the tire; The flexible monitoring belt includes a flexible circuit board substrate, a temperature sensor, a pressure sensor, a triaxial acceleration sensor and a wireless communication unit encapsulated on the flexible circuit board substrate; The temperature sensor is used to monitor the internal temperature of the tire, the pressure sensor is used to measure the air pressure in the tire, and the three-axis acceleration sensor is used to detect the longitudinal, lateral and vertical acceleration of the tire; The wireless communication unit includes a dual-mode transmission module that supports a main channel for a short-range wireless protocol and a backup channel for a long-range wireless protocol, and is used to transmit data from temperature sensors, pressure sensors, and three-axis acceleration sensors to the vehicle control unit and the cloud.

10. The tire for recording driving data for new energy vehicles according to claim 9, characterized in that: The three-axis acceleration sensor is connected to the temperature sensor, and when the longitudinal acceleration is detected to exceed a preset vehicle speed threshold, the sampling frequency of the temperature sensor is increased.

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